Adaptive neural upsampling system for decoding lossy compressed data streams

Abstract

A system and method for enhancing lossy compressed data. The system receives a compressed data stream, decompresses it, and enhances the decompressed data using adaptive neural network models. Key features include data characteristic analysis, dynamic model selection from multiple specialized neural networks, and quality estimation with feedback-driven optimization. The system adapts to various data types and compression levels, recovering lost information without detailed knowledge of the compression process. It implements online learning for continuous improvement and includes security measures to ensure data integrity. The method is applicable to diverse data types, including financial time-series, images, and audio. By combining efficient decompression with advanced neural upsampling, the system achieves superior reconstruction of lossy compressed data, enabling improved data transmission, storage, and analysis in bandwidth-constrained or storage-limited environments while maintaining data quality and security.

What is claimed is: 
     
       1. A system for decoding and enhancing lossy compressed data streams, comprising:
 a computing device comprising at least a memory and a processor; 
 a plurality of programming instructions stored in the memory and operable on the processor, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to:
 receive a compressed data stream; 
 decompress the compressed data stream to produce a decompressed data stream; 
 enhance the decompressed data stream using at least one neural network model to produce an enhanced data stream; 
 estimate the quality of the enhanced data stream; and 
 output the enhanced data stream; 
 
 wherein the at least one neural network model comprises an adaptive neural upsampler configured to recover information lost in compression by leveraging correlations between subsets of the compressed data stream. 
 
     
     
       2. The system of  claim 1 , wherein decompressing the compressed data stream comprises using a Huffman decoder. 
     
     
       3. The system of  claim 1 , wherein the computing device is further caused to analyze characteristics of the decompressed data stream. 
     
     
       4. The system of  claim 3 , wherein the computing device is further caused to select an appropriate neural network model from a plurality of neural network models based on the analyzed characteristics. 
     
     
       5. The system of  claim 1 , wherein enhancing the decompressed data stream comprises recovering information lost during lossy compression. 
     
     
       6. The system of  claim 1 , wherein the computing device is further caused to adjust the neural network model based on the estimated quality of the enhanced data stream. 
     
     
       7. The system of  claim 1 , wherein the computing device is further caused to implement security measures to ensure data integrity throughout the enhancement process. 
     
     
       8. The system of  claim 1 , wherein the compressed data stream comprises financial time-series data. 
     
     
       9. The system of  claim 1 , wherein the neural network model is trained using pairs of original uncompressed data and corresponding lossy compressed data. 
     
     
       10. The system of  claim 1 , wherein the computing device is further caused to perform online learning to adapt the neural network model based on recent data streams. 
     
     
       11. The system of  claim 1 , wherein estimating the quality of the enhanced data stream comprises comparing the enhanced data stream to a predicted original data stream. 
     
     
       12. The system of  claim 1 , wherein the computing device is further caused to iteratively reprocess the decompressed data stream with different neural network models if the estimated quality falls below a predetermined threshold. 
     
     
       13. The system of  claim 1 , wherein the neural network model is a neural upsampler configured to increase the resolution or quality of the decompressed data stream. 
     
     
       14. The system of  claim 1 , wherein the computing device is further caused to detect the level of compression in the received compressed data stream and adjust the enhancement process accordingly. 
     
     
       15. The system of  claim 1 , wherein the system operates in real-time on streaming data. 
     
     
       16. A method for decoding and enhancing lossy compressed data streams, comprising the steps of:
 receiving a compressed data stream; 
 decompressing the compressed data stream to produce a decompressed data stream; 
 enhancing the decompressed data stream using at least one neural network model to produce an enhanced data stream; 
 estimating the quality of the enhanced data stream; and 
 
       output the enhanced data stream;
 wherein the at least one neural network model comprises an adaptive neural upsampler configured to recover information lost in compression by leveraging correlations between subsets of the compressed data stream. 
 
     
     
       17. The method of  claim 16 , wherein decompressing the compressed data stream comprises using a Huffman decoder. 
     
     
       18. The method of  claim 16 , further comprising the step of analyzing characteristics of the decompressed data stream. 
     
     
       19. The method of  claim 18 , further comprising the step of selecting an appropriate neural network model from a plurality of neural network models based on the analyzed characteristics. 
     
     
       20. The method of  claim 16 , wherein enhancing the decompressed data stream comprises recovering information lost during lossy compression. 
     
     
       21. The method of  claim 16 , further comprising the step of adjusting the neural network model based on the estimated quality of the enhanced data stream. 
     
     
       22. The method of  claim 16 , further comprising the step of implementing security measures to ensure data integrity throughout the enhancement process. 
     
     
       23. The method of  claim 16 , wherein the compressed data stream comprises financial time-series data. 
     
     
       24. The method of  claim 16 , wherein the neural network model is trained using pairs of original uncompressed data and corresponding lossy compressed data. 
     
     
       25. The method of  claim 16 , further comprising the step of performing online learning to adapt the neural network model based on recent data streams. 
     
     
       26. The method of  claim 16 , wherein estimating the quality of the enhanced data stream comprises comparing the enhanced data stream to a predicted original data stream. 
     
     
       27. The method of  claim 16 , wherein the neural network model is a neural upsampler configured to increase the resolution or quality of the decompressed data stream.